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Research article
Yong Li
School of Information and Mathematics, Yangtze University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Tuberculosis (TB) which is a preventable and curable disease, is claimed as the second largest number of fatalities and there are 9,029 cases of American in 2018. Many researches have done many study to control TB and had evident e_ects, but TB is also a serious problem for human being. So the study is always improving. Methods: In the paper, we propose a new dynamic model to study the transmission dynamic and prevalence of TB, then use global di_erential evolution and local sequential quadratic programming (DESQP) optimization algorithm to estimate parameters of the model. Next, we use Latin hypercube sampling (LHS) to sample and partial rank correlation coe_cients (PRCC) to analyze the inuence of parameters on the basic reproduction number (R0) and the total infectious (including the diagnosed, undiagnosed and incomplete treatment infectious), respectively. Results: With the PRCC and p-value of the parameters, we _nd how the factors a_ect the outbreaks of TB. Chemoprophylaxis, treatment and vaccination have positive e_ects, relatively, the vaccine expiry date, diagnostic techniques and the contact ratio have negative e_ects. Especially, chemoprophylaxis is the most sensitive factors controlling TB. Conclusion: With results, we give some suggestions to control the prevalence of TB, such as prolonging the duration of the vaccine by researching new and better vaccines to prevent TB, persuading people infected with TB in the latent stage to use chemoprophylaxis to treat and do not contact with the infected and instructing people take care of themselves and be treated in time when they are infected with TB. By doing these, we can e_ectively control and prevent the prevalence of TB according to the epidemiological characteristics of tuberculosis transmission.
Keywords
Tuberculosis (TB), Latin hypercube sampling (LHS), Partial rank correlation coefficients (PRCC), Basic reproduction number, Parameter estimation, Prevention and control measures
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CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Minor revision
On 30 Jun, 2020
Review #2 received
Received 23 Jun, 2020
Review #1 received
Received 23 Jun, 2020
Reviewer #2 agreed
On 16 Jun, 2020
Reviewers invited
Invitations sent on 16 Jun, 2020
Reviewer #1 agreed
On 16 Jun, 2020
Editor assigned
On 15 Jun, 2020
Submission checks complete
On 14 Jun, 2020
Editor invited
On 14 Jun, 2020
No comments provided
Editorial decision: Major revision
On 11 Jun, 2020
Editor assigned
On 10 Jun, 2020
Submission checks complete
On 09 Jun, 2020
Editor invited
On 09 Jun, 2020
Version 1
Posted 02 Dec, 2019
No comments provided
Editorial decision: Major revision
On 12 May, 2020
Review #2 received
Received 01 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Review #1 received
Received 07 Feb, 2020
Reviewer #1 agreed
On 24 Jan, 2020
Reviewers invited
Invitations sent on 05 Jan, 2020
Editor assigned
On 19 Nov, 2019
Submission checks complete
On 18 Nov, 2019
Editor invited
On 18 Nov, 2019
First submitted
On 17 Nov, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Policy
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Public Health.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Yong Li
School of Information and Mathematics, Yangtze University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Public Health.
No community comments so far
Editorial decision: Minor revision
On 30 Jun, 2020
Review #2 received
Received 23 Jun, 2020
Review #1 received
Received 23 Jun, 2020
Reviewer #2 agreed
On 16 Jun, 2020
Reviewers invited
Invitations sent on 16 Jun, 2020
Reviewer #1 agreed
On 16 Jun, 2020
Editor assigned
On 15 Jun, 2020
Submission checks complete
On 14 Jun, 2020
Editor invited
On 14 Jun, 2020
No comments provided
Editorial decision: Major revision
On 11 Jun, 2020
Editor assigned
On 10 Jun, 2020
Submission checks complete
On 09 Jun, 2020
Editor invited
On 09 Jun, 2020
Version 1
Posted 02 Dec, 2019
No comments provided
Editorial decision: Major revision
On 12 May, 2020
Review #2 received
Received 01 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Review #1 received
Received 07 Feb, 2020
Reviewer #1 agreed
On 24 Jan, 2020
Reviewers invited
Invitations sent on 05 Jan, 2020
Editor assigned
On 19 Nov, 2019
Submission checks complete
On 18 Nov, 2019
Editor invited
On 18 Nov, 2019
First submitted
On 17 Nov, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Policy
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Public Health.
Background: Tuberculosis (TB) which is a preventable and curable disease, is claimed as the second largest number of fatalities and there are 9,029 cases of American in 2018. Many researches have done many study to control TB and had evident e_ects, but TB is also a serious problem for human being. So the study is always improving. Methods: In the paper, we propose a new dynamic model to study the transmission dynamic and prevalence of TB, then use global di_erential evolution and local sequential quadratic programming (DESQP) optimization algorithm to estimate parameters of the model. Next, we use Latin hypercube sampling (LHS) to sample and partial rank correlation coe_cients (PRCC) to analyze the inuence of parameters on the basic reproduction number (R0) and the total infectious (including the diagnosed, undiagnosed and incomplete treatment infectious), respectively. Results: With the PRCC and p-value of the parameters, we _nd how the factors a_ect the outbreaks of TB. Chemoprophylaxis, treatment and vaccination have positive e_ects, relatively, the vaccine expiry date, diagnostic techniques and the contact ratio have negative e_ects. Especially, chemoprophylaxis is the most sensitive factors controlling TB. Conclusion: With results, we give some suggestions to control the prevalence of TB, such as prolonging the duration of the vaccine by researching new and better vaccines to prevent TB, persuading people infected with TB in the latent stage to use chemoprophylaxis to treat and do not contact with the infected and instructing people take care of themselves and be treated in time when they are infected with TB. By doing these, we can e_ectively control and prevent the prevalence of TB according to the epidemiological characteristics of tuberculosis transmission.
This is a list of supplementary files associated with this preprint. Click to download.
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